The data science field is still hot and the programming languages that have been used for it are too: R popularity has been increasing every year and especially Python gaining more and more importance in the data science industry. This is not only because this general-purpose language stands out for its readability and has a relatively low and very gradual learning curve, but mostly also thanks to the tools and the concepts that were originally built by scientists and sysadmins. Because, even though there seems to be a culture gap between those who use Python for scientific purposes and those who use it for more conventional purposes such as system administration and web development, their strength is that they can work together. And this has been proven in recent years: the solid foundations of the Python language have been enriched with the creation and development of packages that help data scientists and data science teams tackle complex data problems.
As a result, there are users who look to move more towards using Python for data science. And those who are learning data science often wonder what else they can do with this popular programming language.
From Web Development to Data Science with Python
That's why DataCamp took a look at the differences between Python web development and Python data science, trying to establish what it really takes to make the switch between the two. The results are summarized in our latest infographic “From Web Development to Data Science with Python”:
In this infographic, you will find the differences between the role of the web developer and the data scientist. We took at look at the core strengths of both, the frameworks or libraries and the IDEs both are using, what you can do to learn and practice Python for web development and data science, how you can integrate well within the web development and data science communities, which companies use Python for web development and data science and much more. Hopefully, this infographic will help you to better understand the different aspects that you would have to consider to make the switch from one to the other.
If you want to learn more on data science, make sure to check out DataCamp’s interactive R and data science tutorials and join 220,000 data enthusiasts! Are you interested in some of our other infographics? Check out Statistical Language Wars, Learn Data Science and R vs Python, and the Data Science Industry.
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